The machine learning is the study of statistical models and algorithms in a scientific setting. In this study, we have employed unsupervised algorithms such as KNN and matrix factorization as machine learning methods. The book’s dataset is utilized in the book recommendation system. Suggestion technique is a sorting procedure that was formerly utilized for group selection and material-based sorting. A pattern filtering technique is used to direct a customer to the “rank” or “first option” of an element. The information gathered during the suggestion process was about either the customer’s initial choice on an odd subject related to films, literature, travel, television, and business, among other things. On the other hand, a good book suggestion system design incorporates the customer’s prior scores or background. The method of measuring and processing categories across user opinions is known as cooperative sorting. Collaborative filtering collects numerous users’ book ranks or preferences, and then recommends books to different people depending on their previous interests and preferences. K-Means on the book dataset, or K-Nearest Neighbor is used to produce the best possible result. The information is spread and ends in a large number of matrices in previous methodologies, whereas the information is collected throughout the suggested technique and finishes in a small number of groupings. Based on a variety of characteristics, the preferred framework predicts a customer’s demand for a book. These customers’ perceptions of one another will be influenced.